How to Master AI-powered Sentiment Analysis in 2023?

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How to Master AI-powered Sentiment Analysis in 2023?


We can definitely tell that with the development of e-commerce, SaaS tools, and digital technologies, sentiment analysis is becoming more and more popular. So here’s a guide to sentiment analysis.


1-What is sentiment analysis?

Sentiment analysis (also known as opinion mining, or emotion AI) is a method of analyzing text data to identify its intent.

The goal is to automatically recognize and categorize opinions expressed in the text to determine overall sentiment.

Sentiment analysis definition

Sentiment analysis is the process of analyzing on;ine text to determine the emotional tone they carry. It aims to detect whether sentiment around a brand or topic is positive, negative, or neutral. Simply put, sentiment analysis determines how the author feels about a certain topic.

Positive sentiment may be expressed using words such as “good”, “great”, “wonderful”, and “fantastic”.

Negative sentiment may be expressed using words such as “bad”, “terrible”, “hate”, and “disgusting”.

What is more, you can conduct analysis for any topic you want.

All you need to do is set up a project using a tool and track the keywords that matter to you.

Thanks to performing sentiment analysis, you will be able to:

  • Get a better understanding of how your customers feel about your brand
  • Gain insights that will help you improve your products and services
  • Make your business more responsive to customer feedback
  • React quickly to negative sentiment and turn it around
  • Monitor your brand’s reputation in real-time
  • Keep your customers happy by always putting their feelings first

2-Natural language processing (NLP) sentiment analysis

With the rapid growth of the Internet – a primary source of information and place for opinion sharing – a necessity arises to gather and analyze mentions on a given topic.

Massive data collection is achievable using Internet Monitoring Tools. However, manual analysis of tens of thousands of texts is time and resource-consuming – and this is where Artificial Intelligence (AI) becomes extremely useful.

Natural Language Processing (NLP), part of AI dealing with text analytics, is an essential technique in the modern world to discover the unknown from Internet Monitoring results.

One of the most useful NLP tasks is sentiment analysis – a method for the automatic detection of emotions behind the text.

Sentiment can be analyzed at different levels – from identifying positive or negative opinions, quantifying the level of positivity or negativity, to even identifying the fine-grained emotion behind the opinion (e.g., happiness, anger, sadness, etc.).

3-How to do sentiment analysis?

Performing accurate sentiment analysis without using an online tool can be difficult.

Why is that?

Conducting analysis based on a large volume of data is time-consuming.

Sure, you can try to research and analyze mentions about your business on your own, but it will take lots of your time and energy. Furthermore, the risk of human error is quite significant in that case.

Let’s take a look at the example.

In the last 30 days, the Nike brand gained over 428k mentions.

Can you imagine analyzing each of them and judging whether it has negative or positive sentiment?

4-What is a sentiment score?


Sentiment score detects emotions and assigns them sentiment scores, for example, from 0 up to 10 – from the most negative to most positive sentiment. Sentiment score makes it simpler to understand how customers feel.

There are various ways to calculate a sentiment score, but the most common method is to use a dictionary of negative, neutral, or positive words. The text is then analyzed to see how many negative and positive words it contains. This can give us a good idea of the overall sentiment of the text.

To calculate a sentiment score, various factors are taken into account, such as the number and type of emotions expressed, the strength of those emotions, and the context in which they are used. Sentiment scores can be useful for a variety of purposes, such as calculating customer satisfaction or determining whether a text is positive or negative in nature.

5-Why is it worth using a dedicated tool for sentiment analysis?

The tool will do the job for you. Actually, there are lots of reasons why it is worth using it.

First and foremost, with a proper tool, you will be able to detect positive and negative sentiments easily.

Secondly, it saves time and effort because the process of sentiment extraction is fully automated – it’s the algorithm that analyses the sentiment datasets, therefore human participation is sparse.

Can you imagine browsing the web, finding relevant texts, reading them, and assessing the tone they carry manually? It’s doable but takes ages.

Thirdly, it’s becoming a more and more popular topic as artificial intelligence, deep learning, machine learning techniques, and natural language processing technologies are developing.

Fourthly, as the technology develops, sentiment analysis will be more accessible and affordable for the public and smaller companies as well.

And lastly, the tools are becoming smarter every day. The more they’re fed with data, the smarter and more accurate they become in sentiment extraction.

Besides the sentiment analysis system, you will also gain access to many valuable metrics, such as:

5-What can you use sentiment analysis for?


Text analytics and opinion mining find numerous applications in e-commerce, marketing, advertising, politics, market research, and any other research.

Let’s have a closer look at how text analysis benefits these areas.

01 Brand reputation management

The internet is where consumers talk about brands, products, services, share their experiences and recommendations. Social platforms, product reviews, blog posts, and discussion forums are boiling with opinions and comments that, if collected and analyzed, are a source of business information.

When it comes to brand reputation management, sentiment analysis can be used for brand monitoring to analyze the web and social media buzz about a product, a service, a brand, or a marketing campaign.

Online analysis helps to gauge brand reputation and its perception by consumers.

This is how businesses can discover consumer, media and expert attitudes towards their products, services, marketing campaigns and brands expressed on discussion forums, online review sites, news sites, blogs, Twitter and other publicly available online sources.

Brand monitoring is an important area of business for PR specialists and sentiment analysis should be one of their tools for everyday use.


02 Customer feedback

Companies use sentiment analysis to analyze customers’ opinions.

These days, consumers use their social profiles to share both their positive and negative experiences with brands.

A sentiment analysis tool can identify mentions conveying positive pieces of content showing strengths, as well as negative mentions, showing bad reviews and problems users face and write about online.

In some cases, this makes customer service far more attentive and responsive, as the customer support team is informed in real-time about any negative comments. The support folks need to know about any blunders as quickly as possible. Because the mentions get detected extremely quickly, customer service has the advantage of rapid reaction time. This makes customer experience management much more seamless and enjoyable.

Our wonderful content manager, Chia, made a video that sums up how analyzing the sentiment of your customer feedback lets you discover what your customers like and dislike about your company and products.



03 Market research

Sentiment analysis offers a vast set of data, making it an excellent addition to any type of market research.

Whether you’re analyzing entire markets, niches, segments, products, their specific features, or assessing any market buzz, sentiment analysis provides you with tremendous amounts of invaluable information: what consumers like, dislike, or what their expectations are.

All of this data allows you to conduct relatively specific market investigations, making the decision-making process better.

05 Politics

Political scientists have also found a great use for sentiment analysis.

In 2012, using sentiment analysis, the Obama administration investigated the reception of policy announcements during the 2012 presidential election.

During the last presidential election in the US, some organizations analyzed, for example, how many negative mentions about particular candidates appeared in the media and news articles.

There have been at least a few academic papers examining sentiment analysis in relation to politics.

  • Prediction of Indian election on the basis of Twitter sentiment analysis
  • Political Data Science: Analyzing Trump, Clinton, and Sanders Tweets and Sentiment
  • Analysis of political sentiment in presidential elections in Egypt using Twitter data

How does sentiment analysis work?

The science behind the process is based on algorithms of natural language processing and machine learning to categorize pieces of writing as positive, neutral, or negative.

Sentiment analysis might use various types of algorithms.


Such an algorithm relies exclusively on machine learning techniques and learns on received data. Machine learning is the most fundamental aspect of artificial intelligence.

Automatic sentiment analysis starts with creating a dataset that contains a set of texts classified either as positive, negative, or neutral.

With this in place, learning begins and continues as a semi-automatic process. This algorithm learns on data until the system achieves some level of independence, sufficient enough to correctly assess the sentiment of new, unknown texts. It’s then utterly important what data the algorithm is fed with.

If the algorithm hadn’t come across a particular example earlier, it won’t perform an accurate analysis.

One of the biggest advantages of this algorithm is the quantity of data it can analyze – way, way more than the rule-based algorithm.

When it comes to disadvantages, the algorithm makes it difficult to explain decisions behind text analyses, meaning, it’s impossible to tell why it classified a particular text as positive or negative.


This algorithm is based on manually created lexicons that define positive and negative strings of words. The algorithm then analyzes the amounts of positive and negative words to see which ones dominate.

Rules can be set around other aspects of the text, for example, part of speech, syntax, and more.

This approach is easy to implement and transparent when it comes to rules standing behind analyses.


This one combines both of the above mentioned algorithms and seems to be the most effective solution.

It’s because it combines high accuracy provided by machine learning and stability from the rule-based, lexicon-based approach.


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